Patients in a critical care setting are often subject to a significant risk of experiencing adverse outcomes, such as death, stroke, sepsis, coma, wound infection, or other undesired clinical conditions. The ability to precisely assess the risk of an adverse outcome can help to improve the quality of care delivered to these patients by identifying those who are at a significantly increased or decreased risk and then treating or monitoring those patients accordingly. Patients may also welcome having precise estimates of risk available to them for use in discussions with their healthcare providers. A precise assessment of risk may also be utilized for other purposes, such as an acuity adjustment of data.
Health risk assessment systems generally use data from a patient population as “training data” so that the systems can learn how to assess the health risks of a particular patient. The sheer volume of information that has traditionally been collected and analyzed for each patient in the population, however, poses a serious challenge to health care professionals. Caregivers often must keep track of a large variety of patient data, including for example demographics, comorbidities, laboratory results, imaging results, parameters for continuous monitoring, and a record of interventions. While recent advances allow these variables to be combined (for many clinical applications) into models with excellent risk adjustment and prediction, the collection processes for these variables increase health care costs and impose demands on both caregivers and patients. Laboratory data may be particularly invasive, costly and time-intensive to obtain. Moreover, certain types of data relating to a patient population, such as surgical and diagnostic codes, may not be amenable to certain risk prediction techniques despite containing useful information.